Abstract
Process algebras are an effective method for defining models of complex interacting biological processes, but defining a model requires expertise from both modeller and domain expert. In addition, even with the right model, tuning parameters to allow model outputs to match experimental data can be difficult. This is the well-known parameter fitting problem. Evolutionary algorithms provide effective methods for finding solutions to optimisation problems with large search spaces and are well suited to investigating parameter fitting problems. We present the Evolving Process Algebra (EPA) framework which combines an evolutionary computation approach with process algebra modelling to produce parameter distribution data that provides insight into the parameter space of the biological system under investigation. The EPA framework is demonstrated through application to a novel example: T helper cell activation in the immune system in the presence of co-infection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bäck, T., Fogel, D., Michalewicz, Z.: Evolutionary Computation, vol. 1,2. Taylor & Francis (2000)
Baeten, J.: A brief history of process algebra. Theoretical Computer Science 335(2/3), 131–146 (2005)
Bernardo, M., Degano, P., Zavattaro, G. (eds.): SFM 2008. LNCS, vol. 5016. Springer, Heidelberg (2008)
Centre for Systems Biology at Edinburgh: Systems biology software infrastructure (2011), http://www.sbsi.ed.ac.uk/
Chou, I.C., Voit, E.O.: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences 219, 57–83 (2009)
Ciocchetta, F., Hillston, J.: Bio-PEPA: a Framework for the Modelling and Analysis of Biochemical Networks. Theoretical Computer Science 410, 3065–3084 (2009)
Cohen, J.: The crucial role of CS in systems and synthetic biology. Communications of the Association for Computing Machinery 51, 15–18 (2008)
De Jong, K.A.: Evolutionary computation - a unified approach. MIT Press (2006)
Duguid, A., Gilmore, S., Guerriero, M., Hillston, J., Loewe, L.: Design and development of software tools for Bio-PEPA. In: Proc. of Winter Simulation Conference 2009, pp. 956–967 (2009)
Finkleman, F.D., Shea-Donohue, T., Goldhill, J., Sullivan, C.A., Morris, S.C., Madden, K.B., Gause, W.C., Urban Jr., J.F.: Cytokine regulation of host defense against parasitic gastrointestinal nematodes: lessons from studies with rodent models. Annual Review of Immunology 15, 505–533 (1997)
Fogel, G.B., Corne, D.W.: Evolutionary Computation in Bioinformatics. Morgan-Kaufmann (2003)
Fraser, A.: Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Sciences 10, 484–491 (1957)
Gibson, M.A., Bruck, J.: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. Journal of Physical Chemistry 104, 1876–1889 (2000)
Goff, L.L., Lamb, J., Graham, A., Harcus, Y., Allen, J.E.: IL-4 is required to prevent filarial nematode development in resistant but not susceptible strains of mice. International Journal for Parasitology 32, 1277–1284 (2002)
Goldberg, D.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley (1989)
Hartgers, F., Yazdanbakhsh, M.: Co-infection of helminths and malaria: modulation of the immune responses to malaria. Parasite Immunology 28, 497–506 (2006)
Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press (1996)
Himmelspach, J., Ewald, R., Uhrmacher, A.M.: A Flexible and Scalable Experimentation Layer. In: Mason, S.J., Hill, R.R., Mnch, L., Rose, O., Jefferson, T., Fowler, J.W. (eds.) Proceedings of the 2008 Winter Simulation Conference, pp. 827–835. IEEE (2008)
Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press (1992)
Luke, S.: Essentials of Metaheuristic, Lulu (2009), http://cs.gmu.edu/~sean/book/metaheuristics/
Machado, D., Costa, R., Rocha, M., Ferreira, E., Tidor, B., Rocha, I.: Modeling formalisms in systems biology. AMB Express 5, 1–45 (2011)
Mahajan, S., Gray, D., Harnett, W., Graham, A., Allen, J.: Helminth-induced unmodified Th2 cells affect malaria-induced immune responses but alter disease little (2010) unpublished draft
Marco, D., Cairns, D., Shankland, C.: Optimisation of process algebra models using evolutionary computation. In: Proceedings of 2011 IEEE Congress on Evolutionary Computation, pp. 1296–1301. IEEE (2011)
Marco, D., Shankland, C., Cairns, D.: Evolving bio-pepa process algebra models using genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 177–183. ACM (2012)
McCaig, C., Begon, M., Norman, R., Shankland, C.: A rigorous approach to investigating common assumptions about disease transmission: Process algebra as an emerging modelling methodology for epidemiology. Theory in Biosciences 130, 19–29 (2011), special issue on emerging modelling methodologies
Prandi, D.: Particle swarm optimization for stochastic process calculi. In: Proceedings of the 9th Workshop on Process Algebra and Stochastically Timed Activities, Department of Computing, pp. 77–82. Imperial College, London (2010)
Priami, C.: Process calculi and life science. Electronic Notes in Theoretical Computer Science 162, 301–304 (2006)
Ross, B.J., Imada, J.: Evolving stochastic processes using feature tests and genetic programming. In: 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1059–1066. ACM (2009)
Stockwell, D.: Genetic algorithms II. In: Machine Learning Methods for Ecological Applications, pp. 123–144. Kluwer Academic Publishers (1999)
Zhu, J., Paul, W.: Heterogeneity and plasticity of T helper cells. Cell Research 20, 4–12 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marco, D. et al. (2012). Investigating Co-infection Dynamics through Evolution of Bio-PEPA Model Parameters: A Combined Process Algebra and Evolutionary Computing Approach. In: Gilbert, D., Heiner, M. (eds) Computational Methods in Systems Biology. CMSB 2012. Lecture Notes in Computer Science(), vol 7605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33636-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-33636-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33635-5
Online ISBN: 978-3-642-33636-2
eBook Packages: Computer ScienceComputer Science (R0)